class_docstr: Principal Component Analysis (PCA).
  
  Parameters
  ----------
  rescale_with_mean
      Whether or not to subtract each column's mean before performing SVD.
  rescale_with_std
      Whether or not to standardize each column before performing SVD.
  n_components
      The number of principal components to compute.
  n_iter
      The number of iterations used for computing the SVD.
  copy
      Whether nor to perform the computations inplace.
  check_input
      Whether to check the coherence of the inputs or not.
code: def column_contributions_(self):
      return (
          self.column_coordinates_.loc[self.feature_names_in_] ** 2
      ).div(self.eigenvalues_, axis=1)
  
imports: from __future__ import annotations
  import functools
  import altair
  import numpy
  import pandas
  import sklearn.base
  import sklearn.utils
  from sklearn import preprocessing
  from prince import svd, utils
label_desc: Use image node_id 14 for calling the PCA obj's underlying member method code with example usage: obj.column_contributions_() and returns: unknown